


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__UpperCamelCase     :    Dict 	=				logging.get_logger(__name__)

__UpperCamelCase     :    Dict 	=				{
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}



class 				_UpperCamelCase			(    __lowerCAmelCase    ):

   '''simple docstring'''







   a_					:				Optional[int]     =			"table-transformer"
   a_					:				List[str]     =			["past_key_values"]
   a_					:				Any     =			{
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
   }

   def __init__(	self	:			Dict							,	_lowerCamelCase	:			Dict=True							,	_lowerCamelCase	:			Union[str, Any]=None							,	_lowerCamelCase	:			List[Any]=3							,	_lowerCamelCase	:			List[str]=1_0_0							,	_lowerCamelCase	:			Optional[Any]=6							,	_lowerCamelCase	:			Optional[int]=2_0_4_8							,	_lowerCamelCase	:			Optional[Any]=8							,	_lowerCamelCase	:			int=6							,	_lowerCamelCase	:			Optional[Any]=2_0_4_8							,	_lowerCamelCase	:			List[Any]=8							,	_lowerCamelCase	:			Optional[Any]=0.0							,	_lowerCamelCase	:			List[str]=0.0							,	_lowerCamelCase	:			Union[str, Any]=True							,	_lowerCamelCase	:			Optional[Any]="relu"							,	_lowerCamelCase	:			List[str]=2_5_6							,	_lowerCamelCase	:			Optional[int]=0.1							,	_lowerCamelCase	:			int=0.0							,	_lowerCamelCase	:			str=0.0							,	_lowerCamelCase	:			Optional[Any]=0.02							,	_lowerCamelCase	:			Tuple=1.0							,	_lowerCamelCase	:			Union[str, Any]=False							,	_lowerCamelCase	:			int="sine"							,	_lowerCamelCase	:			Union[str, Any]="resnet50"							,	_lowerCamelCase	:			Optional[int]=True							,	_lowerCamelCase	:			Any=False							,	_lowerCamelCase	:			List[str]=1							,	_lowerCamelCase	:			List[str]=5							,	_lowerCamelCase	:			Any=2							,	_lowerCamelCase	:			Union[str, Any]=1							,	_lowerCamelCase	:			Optional[int]=1							,	_lowerCamelCase	:			Optional[Any]=5							,	_lowerCamelCase	:			Tuple=2							,	_lowerCamelCase	:			Any=0.1							,	**_lowerCamelCase	:			Any							,	):






         '''simple docstring'''



         if backbone_config is not None and use_timm_backbone:
               raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""				)

         if not use_timm_backbone:
               if backbone_config is None:
                     logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""				)
                     __lowerCamelCase  :   int							    =    CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]				)
               elif isinstance(lowerCamelCase_							,	lowerCamelCase_				):
                     __lowerCamelCase  :   Optional[Any]							    =    backbone_config.get("""model_type"""				)
                     __lowerCamelCase  :   List[Any]							    =    CONFIG_MAPPING[backbone_model_type]
                     __lowerCamelCase  :   Any							    =    config_class.from_dict(lowerCamelCase_				)
               # set timm attributes to None
               __lowerCamelCase    ,			__lowerCamelCase    ,			__lowerCamelCase  :   Optional[Any]							    =    None, None, None

         __lowerCamelCase  :   Optional[int]							    =    use_timm_backbone
         __lowerCamelCase  :   Optional[int]							    =    backbone_config
         __lowerCamelCase  :   str							    =    num_channels
         __lowerCamelCase  :   Union[str, Any]							    =    num_queries
         __lowerCamelCase  :   Optional[int]							    =    d_model
         __lowerCamelCase  :   Any							    =    encoder_ffn_dim
         __lowerCamelCase  :   Tuple							    =    encoder_layers
         __lowerCamelCase  :   Dict							    =    encoder_attention_heads
         __lowerCamelCase  :   str							    =    decoder_ffn_dim
         __lowerCamelCase  :   int							    =    decoder_layers
         __lowerCamelCase  :   Union[str, Any]							    =    decoder_attention_heads
         __lowerCamelCase  :   Dict							    =    dropout
         __lowerCamelCase  :   List[str]							    =    attention_dropout
         __lowerCamelCase  :   Optional[int]							    =    activation_dropout
         __lowerCamelCase  :   Optional[Any]							    =    activation_function
         __lowerCamelCase  :   Any							    =    init_std
         __lowerCamelCase  :   List[Any]							    =    init_xavier_std
         __lowerCamelCase  :   int							    =    encoder_layerdrop
         __lowerCamelCase  :   Tuple							    =    decoder_layerdrop
         __lowerCamelCase  :   Dict							    =    encoder_layers
         __lowerCamelCase  :   Optional[int]							    =    auxiliary_loss
         __lowerCamelCase  :   int							    =    position_embedding_type
         __lowerCamelCase  :   int							    =    backbone
         __lowerCamelCase  :   Optional[Any]							    =    use_pretrained_backbone
         __lowerCamelCase  :   Dict							    =    dilation
         # Hungarian matcher
         __lowerCamelCase  :   List[Any]							    =    class_cost
         __lowerCamelCase  :   List[str]							    =    bbox_cost
         __lowerCamelCase  :   Union[str, Any]							    =    giou_cost
         # Loss coefficients
         __lowerCamelCase  :   Union[str, Any]							    =    mask_loss_coefficient
         __lowerCamelCase  :   Union[str, Any]							    =    dice_loss_coefficient
         __lowerCamelCase  :   Optional[int]							    =    bbox_loss_coefficient
         __lowerCamelCase  :   Any							    =    giou_loss_coefficient
         __lowerCamelCase  :   Any							    =    eos_coefficient
         super().__init__(is_encoder_decoder=lowerCamelCase_							,	**lowerCamelCase_				)

   @property
   def 	_snake_case     (	self	:			Dict				):






         '''simple docstring'''



         return self.encoder_attention_heads


   @property
   def 	_snake_case     (	self	:			Any				):






         '''simple docstring'''



         return self.d_model



class 				_UpperCamelCase			(    __lowerCAmelCase    ):

   '''simple docstring'''







   a_					:				Dict     =			version.parse("1.11"    )

   @property
   def 	_snake_case     (	self	:			Union[str, Any]				):






         '''simple docstring'''



         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                 ("""pixel_mask""", {0: """batch"""}),
             ]				)

   @property
   def 	_snake_case     (	self	:			List[str]				):






         '''simple docstring'''



         return 1E-5


   @property
   def 	_snake_case     (	self	:			Tuple				):






         '''simple docstring'''



         return 1_2




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
     import torch

     from transformers.generation import (
         MaxLengthCriteria,
         MaxNewTokensCriteria,
         MaxTimeCriteria,
         StoppingCriteriaList,
         validate_stopping_criteria,
     )
@require_torch
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      3
    UpperCamelCase			  =      250

    UpperCamelCase			  =      ids_tensor((batch_size, length)   ,   lowerCamelCase_				)
    UpperCamelCase			  =      torch.ones((batch_size, length)   ,   device=lowerCamelCase_   ,   dtype=torch.float				) / length
    return input_ids, scores







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      StoppingCriteriaList(
        [
            MaxLengthCriteria(max_length=10				),
            MaxTimeCriteria(max_time=0.1				),
        ]				)

    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxLengthCriteria(max_length=10				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxNewTokensCriteria(start_length=5   ,   max_new_tokens=5				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      StoppingCriteriaList([criteria]				)
    self.assertEqual(criteria_list.max_length   ,   10				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1   ,   initial_timestamp=time.time() - 0.2				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   10				)

    with self.assertWarns(lowerCamelCase_				):
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   11				)

    UpperCamelCase			  =      validate_stopping_criteria(StoppingCriteriaList()   ,   11				)

    self.assertEqual(len(lowerCamelCase_				)   ,   1				)
